Hi Frank, many thanks for your Feedback! I try to answer your questions: we collected online process data of one parameter from several experiments. we used the FDE to analyse the data, to create a functional model and to get the FPCs for the subsequent analysis The FPCs were finally linked with the product concentration offline data in order to predict the concentration based on the FPCs respectively on the online process data In the first part of the poster (left site), the variances in the online process data were correlated with the product concentration using the FDE. In the second part (right site) we predicted the product concentration of a running process based on the historical data. The historical data were used to create a prediction model for the product concentration, which is based on the FPCs from FDE analysis. We took the current available data of the running process at different time points, included this data into the FDE model, extracted the specific FPCs values of the running process and entered this data in the existing prediction model. In this way, it was possible to predict the product concentration at different time points of the running process. Best wishes Sven
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Sven Steinbusch, Senior Project Leader Upstream, Lonza AG Martin Demel, JMP Senior Systems Engineer, SAS
The ability to explore the knowledge behind data will be an important strategic asset in the future. For offline data, statistical analysis are well established and are used extensively. The statistical analysis of online data and its correlation to the respective offline data is highly complex. However, the implementation of new data analysis tools in JMP Pro provides a way to make online data readily available for statistical analysis.
Using the Functional Data Explorer (FDE) and the Generalised Regression application, we successfully analysed data of a biopharmaceutical process and discovered hidden and unexpected correlations between online and offline data. These new tools allow us to gain more understanding of our processes, which in turn will allow us to better identify options in order to optimize our processes.
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